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Record W4407729438 · doi:10.1088/1361-6587/adb78a

Reaching extreme laser intensities using multi-petawatt lasers interacting with micro-cones

2025· article· en· W4407729438 on OpenAlexfundno aff
Olimpia Budrigă, E. d’Humières, Aurora Budriga, K. A. Tanaka

Bibliographic record

VenuePlasma Physics and Controlled Fusion · 2025
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
FundersNuclear PhysicsEuropean Regional Development FundJapan Society for the Promotion of ScienceAlliance de recherche numérique du CanadaEuropean CommissionNational University of Science and TechnologyMinisterul Cercetării, Inovării şi Digitalizării
KeywordsLaserOpticsPhysicsMaterials science

Abstract

fetched live from OpenAlex

Abstract Micro-cones so far mainly used for high energy density physics research have been proven to have an effective control on the fast electrons in the context of fast ignition research. In this paper we demonstrate by performing three-dimensional particle-in-cell simulations that an ultra-high intensity laser pulse can be intensified 28 times at the interaction with plastic micro-cones. The extreme intensities of the focused laser which are reached at the interaction with the plastic micro-cones are important in the plasma and nuclear physics investigations of dark matter, non-linear quantum electrodynamics, and fission–fusion experiments to study the N = 126 waiting point for better understanding of the Universe. Furthermore, we observe that micro-cones can shorten ultra-high intensity laser pulses both in time and space. The highest intensification of the incident laser pulse varies in time but not in position being localized very close to the rear side of the micro-cone tip. Therefore, the micro-cone can be a useful device in relativistic plasma optics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.220
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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